Multiple regression analysis for factors independently contributing to MV.

<p>Multiple regression analysis for factors independently contributing to MV.</p

Relationship between tissue-area mean blur rate (MT) and clinical findings.

<p>There was no significant difference in MT between the female and male subjects (left). MT was weakly correlated with IOP (r = -0.29, <i>P</i> = 0.001, right).</p

Characteristics of healthy subjects.

<p>Characteristics of healthy subjects.</p

Relationship between vessel-area mean blur rate (MV) and clinical findings.

<p>MV was higher in the female subjects than in the male subjects (<i>P</i> < 0.05, left). MV was weakly correlated with age (r = -0.24, <i>P</i> < 0.001, right).</p

Relationship between overall mean blur rate (MA) and clinical findings.

<p>MA was higher in the female subjects than in the male subjects (<i>P</i> < 0.01, left). MA was not correlated with age or IOP (right).</p

Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters

<div><p>Purpose</p><p>This study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments.</p><p>Methods</p><p>This study enrolled 163 eyes of 105 OAG patients (age: 62.3 ± 12.6, mean deviation of Humphrey field analyzer: -8.9 ± 7.5 dB). The eyes were classified into Nicolela’s 4 optic disc types by 3 glaucoma specialists. Randomly, 114 eyes were selected for training data and 49 for test data. A neural network (NN) was trained with the training data and evaluated with the test data. We used 91 types of quantitative data, including 7 patient background characteristics, 48 quantified OCT (swept-source OCT; DRI OCT Atlantis, Topcon) values, including optic disc topography and circumpapillary retinal nerve fiber layer thickness (cpRNFLT), and 36 blood flow parameters from laser speckle flowgraphy, to build the machine learning classification model. To extract the important features among 91 parameters, minimum redundancy maximum relevance and a genetic feature selection were used.</p><p>Results</p><p>The validated accuracy against test data for the NN was 87.8% (Cohen’s Kappa = 0.83). The important features in the NN were horizontal disc angle, spherical equivalent, cup area, age, 6-sector superotemporal cpRNFLT, average cup depth, average nasal rim disc ratio, maximum cup depth, and superior-quadrant cpRNFLT.</p><p>Conclusion</p><p>The proposed machine learning system has proved to be good identifiers for different disc types with high accuracy. Additionally, the calculated confidence levels reported here should be very helpful for OAG care.</p></div

Correlation between B-scan and <i>en-face</i> images.

<p>(A) B-scan image with dotted lines showing the position of the <i>en-face</i> images below. (B) Upper area of the lamina cribrosa (LC). (C) Upper border of the LC. (D) Centerline of the LC. (E) Lower border of the LC. (F) Lower area of the LC.</p

Comparison of the ratio between the reliably measurable area and the Bruch’s membrane opening area in normal, preperimetric glaucoma, and normal tension glaucoma patients.

<p>BMO: Bruch’s membrane opening. PPG: preperimetric glaucoma. NTG: normal-tension glaucoma. P values were determined with the Kruskal-Wallis test.</p><p>Comparison of the ratio between the reliably measurable area and the Bruch’s membrane opening area in normal, preperimetric glaucoma, and normal tension glaucoma patients.</p

Correlation between average lamina cribrosa (LC) thickness and circumpapillary retinal nerve fiber layer thickness (cpRNFLT).

<p>(A) Bar graph indicating LC thickness in different stages. Note: there were significant differences between these groups (Kruskal-Wallis test followed by Steel-Dwass test). *: P<0.05, **: P<0.01. (B) ROC curve. The area under the ROC curve was 0.9, with a cutoff value of 260.4 μm (sensitivity: 0.83; specificity: 0.89). (C) Scatter plot of cpRNFLT against average LC thickness (avgLCT) in the entire group (N = 54). (D) Scatter plot of MD against avgLCT in the entire group (N = 54). Note: the correlation coefficient of avgLCT and cpRNFLT was 0.64 (p < 0.01) and the correlation coefficient of avgLCT and HFA MD was 0.56 (p < 0.001).</p

Representative B-scan images of normal eyes, preperimetric glaucoma eyes, and eyes with normal-tension glaucoma.

<p>(A-C) B-scan images. (D-F) <i>en-face</i> images. (G-I) Grayscale Humphrey field analyzer image. (J-L) 12 clock-wise sectors of OCT-measured circumpapillary retinal nerve fiber layer thickness. (M-O) Representative lamina cribrosa (LC) thickness map showing the reliably measurable area. (A, D, G, J, M) Normal. (B, E, H, K, N) PPG. (C, F, I, L, O) Normal-tension glaucoma. Note: LC thickness gradually declined with glaucoma severity.</p